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Support half type for pandas. (#8481)

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This commit is contained in:
Jiaming Yuan
2022-11-24 12:47:40 +08:00
committed by GitHub
parent e07245f110
commit 8f97c92541
5 changed files with 109 additions and 53 deletions
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13
tests/python/test_dmatrix.py
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@@ -6,6 +6,7 @@ import pytest
import scipy.sparse
from hypothesis import given, settings, strategies
from scipy.sparse import csr_matrix, rand
from xgboost.testing.data import np_dtypes
import xgboost as xgb
from xgboost import testing as tm
@@ -453,3 +454,15 @@ class TestDMatrix:
np.testing.assert_equal(csr.indptr, ret.indptr)
np.testing.assert_equal(csr.data, ret.data)
np.testing.assert_equal(csr.indices, ret.indices)
def test_dtypes(self) -> None:
n_samples = 128
n_features = 16
for orig, x in np_dtypes(n_samples, n_features):
m0 = xgb.DMatrix(orig)
m1 = xgb.DMatrix(x)
csr0 = m0.get_data()
csr1 = m1.get_data()
np.testing.assert_allclose(csr0.data, csr1.data)
np.testing.assert_allclose(csr0.indptr, csr1.indptr)
np.testing.assert_allclose(csr0.indices, csr1.indices)

47
tests/python/test_predict.py
View File

@@ -5,6 +5,7 @@ import numpy as np
import pandas as pd
import pytest
from scipy import sparse
from xgboost.testing.data import np_dtypes
from xgboost.testing.shared import validate_leaf_output
import xgboost as xgb
@@ -230,46 +231,10 @@ class TestInplacePredict:
from_dmatrix = booster.predict(dtrain)
np.testing.assert_allclose(from_dmatrix, from_inplace)
def test_dtypes(self):
orig = self.rng.randint(low=0, high=127, size=self.rows * self.cols).reshape(
self.rows, self.cols
)
predt_orig = self.booster.inplace_predict(orig)
# all primitive types in numpy
for dtype in [
np.int32,
np.int64,
np.byte,
np.short,
np.intc,
np.int_,
np.longlong,
np.uint32,
np.uint64,
np.ubyte,
np.ushort,
np.uintc,
np.uint,
np.ulonglong,
np.float16,
np.float32,
np.float64,
np.half,
np.single,
np.double,
]:
X = np.array(orig, dtype=dtype)
predt = self.booster.inplace_predict(X)
np.testing.assert_allclose(predt, predt_orig)
# boolean
orig = self.rng.binomial(1, 0.5, size=self.rows * self.cols).reshape(
self.rows, self.cols
)
predt_orig = self.booster.inplace_predict(orig)
for dtype in [np.bool8, np.bool_]:
X = np.array(orig, dtype=dtype)
predt = self.booster.inplace_predict(X)
def test_dtypes(self) -> None:
for orig, x in np_dtypes(self.rows, self.cols):
predt_orig = self.booster.inplace_predict(orig)
predt = self.booster.inplace_predict(x)
np.testing.assert_allclose(predt, predt_orig)
# unsupported types
@@ -278,6 +243,6 @@ class TestInplacePredict:
np.complex64,
np.complex128,
]:
X = np.array(orig, dtype=dtype)
X: np.ndarray = np.array(orig, dtype=dtype)
with pytest.raises(ValueError):
self.booster.inplace_predict(X)

23
tests/python/test_quantile_dmatrix.py
View File

@@ -11,6 +11,7 @@ from xgboost.testing import (
make_categorical,
make_sparse_regression,
)
from xgboost.testing.data import np_dtypes
import xgboost as xgb
@@ -238,3 +239,25 @@ class TestQuantileDMatrix:
np.testing.assert_allclose(
booster.predict(qdm), booster.predict(xgb.DMatrix(qdm.get_data()))
)
def test_dtypes(self) -> None:
n_samples = 128
n_features = 16
for orig, x in np_dtypes(n_samples, n_features):
m0 = xgb.QuantileDMatrix(orig)
m1 = xgb.QuantileDMatrix(x)
csr0 = m0.get_data()
csr1 = m1.get_data()
np.testing.assert_allclose(csr0.data, csr1.data)
np.testing.assert_allclose(csr0.indptr, csr1.indptr)
np.testing.assert_allclose(csr0.indices, csr1.indices)
# unsupported types
for dtype in [
np.string_,
np.complex64,
np.complex128,
]:
X: np.ndarray = np.array(orig, dtype=dtype)
with pytest.raises(ValueError):
xgb.QuantileDMatrix(X)